This study presents an intelligent maximum power point tracking (MPPT) control strategy for variable-speed wind turbine generators, based on the crow search algorithm (CSA) to maximize power generation under wind fluctuations. The proposed CSA-based MPPT method is designed to improve the dynamic response and efficiency of wind energy conversion systems by effectively tracking the optimal operating point. The performance of the CSAbased approach is compared with a conventional torque regulation method, evaluating key metrics such as convergence speed and robustness under turbulent wind conditions. Simulation results demonstrate that the CSA-based MPPT controller outperforms the conventional method, achieving faster convergence to the maximum power point, reduced power oscillations, and improved energy capture efficiency. The results highlight the potential of bio-inspired algorithms like CSA in advancing MPPT control for renewable energy systems, offering a promising alternative to traditional methods for enhancing the performance and reliability of wind turbine generators.